Today’s layout
Qualitative research
“Qualitative researchers strive to understand the meaning people have constructed about their world and their experiences.” (Sharan B. Merriam 2002)
“Qualitative research is an effort to understand situations in their uniqueness as part of a particular context and the interactions there. This understanding is an end in itself.” (Patton 1990)
What are the principles of qualitative research?
The researcher is the primary instrument for data collection and data analysis
The analysis seeks to find emerging themes
The product of a qualitative study is richly descriptive
How might this look?
Sample Selection
Select a sample from which the most can be learned!
Data Collection
Major sources of data – interviews, observations, documents
Data Analysis
Compare units of data to find common patterns across the data
Investigating student learning through code
Warm-up (90 seconds)
How would you describe the action(s) being taken in this statement?
A framework for analyzing student’s code (Schulte 2008)
| Text Surface | Program Execution | Function | |
|---|---|---|---|
| Macrostructure | Understanding the overall structure of the program | Understanding the “algorithm” of the program | Understanding the goal / purpose of the program (in its context) |
| Relations | References between blocks, e.g., method calls, object creation | Sequence of method calls, object sequence diagrams | Understanding how sub-goals are related to goals, how function is achieved by subfunctions |
| Blocks | Regions of interest (ROI) that syntactically or semantically build a unit | Operation of a block, a method, or a ROI (as a sequence of statements) | Function of a block, may be seen as a sub-goal |
| Atoms | Language elements | Operation of a statement | Function of a statement, only understandable in context |
Coding student’s code
Descriptive code
“Filters a vector of values using extraction operator, based on an equality relation with a variable selected from dataframe using
$operator”
In-vivo code
“Uses
[ ]and==to filter vector, uses$to select variable”
Uncovering emergent themes
linearAnterior <- lm(PADataNoOutlier$Lipid ~ PADataNoOutlier$PSUA)
early <- subset(RPMA2Growth, StockYear < 2006)
Weight5 <- mean(RPMA2GrowthSub$Weight[RPMA2GrowthSub$Age == 5], na.rm = TRUE)
gas <- gas[!(substr(gas$sampleID,3,3) %in% c("b","c")), ]
obsD <- subset(gas, gas$carboy == "D")$N15_N2_Ar
lowerCIBound <- pMat[1:mlleIndex,1][which.min(abs(mlleCI+likelihoods[1:mlleIndex]))]Data wrangling
Statements of code whose purpose is to prepare a dataset for analysis and / or visualization
Sub-themes
An alternative direction
Practical considerations
How much code should I collect?
How do readers trust my analysis?
Trust comes from:
How could this be used?
Concept dependence
How does a student’s concept model of a dataset inform how they filter data?
(atoms; program execution)
Program environment
How do the visualizations produced by students who learn ggplot differ from those who learn “base” R?
(blocks; program execution)
Linguistic structure
How do students name objects they will use later?
(relationships; text)
Learning trajectory
How do students’ exploratory data analyses change over the duration of a course?
(macrostructure; function / purpose)
Why is this important for data science education?
Theobold et al. (2023)
How can we distinguish merely interesting learning from effective learning (Wiggins and McTighe 2005)?
Questions?